BackgroundTumour markers are standard tools for the differential diagnosis of cancer. However, the occurrence of nonspecific symptoms and different malignancies involving the same cancer site may lead to a high proportion of misclassifications.Classification accuracy can be improved by combining information from different markers using standard data mining techniques, like Decision Tree (DT), Artificial Neural Network (ANN), and k-Nearest Neighbour (KNN) classifier. Unfortunately, each method suffers from some unavoidable limitations. DT, in general, tends to show a low classification performance, whereas ANN and KNN produce a "black-box" classification that does not provide biological information useful for clinical purposes.MethodsLogic Learning Machine (LLM) is an innovative method of supervised data analysis capable of building classifiers described by a set of intelligible rules including simple conditions in their antecedent part. It is essentially an efficient implementation of the Switching Neural Network model and reaches excellent classification accuracy while keeping low the computational demand.LLM was applied to data from a consecutive cohort of 169 patients admitted for diagnosis to two pulmonary departments in Northern Italy from 2009 to 2011. Patients included 52 malignant pleural mesotheliomas (MPM), 62 pleural metastases (MTX) from other tumours and 55 benign diseases (BD) associated with pleurisies. Concentration of three tumour markers (CEA, CYFRA 21-1 and SMRP) was measured in the pleural fluid of each patient and a cytological examination was also carried out.The performance of LLM and that of three competing methods (DT, KNN and ANN) was assessed by leave-one-out cross-validation.ResultsLLM outperformed all other considered methods. Global accuracy was 77.5% for LLM, 72.8% for DT, 54.4% for KNN, and 63.9% for ANN, respectively. In more details, LLM correctly classified 79% of MPM, 66% of MTX and 89% of BD. The corresponding figures for DT were: MPM = 83%, MTX = 55% and BD = 84%; for KNN: MPM = 58%, MTX = 45%, BD = 62%; for ANN: MPM = 71%, MTX = 47%, BD = 76%.Finally, LLM provided classification rules in a very good agreement with a priori knowledge about the biological role of the considered tumour markers.ConclusionsLLM is a new flexible tool potentially useful for the differential diagnosis of pleural mesothelioma.

is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. ABSTRACTTo better simulate the way designers work, specific tools are needed to handle directly specific shape features meaningful for the design intent, without focusing on the underlying mathematic representation. For this purpose it is fundamental to identify proper higher-level shape descriptors as well as the corresponding manipulation techniques. The paper presents the definition and implementation of semantic operators for curve deformation based on a shape characterization that is specific to the industrial design context. The work grounds on the innovative approach suggested by the FIORES-II project for the intent-driven modeling tools for direct shape modification and on the multi-layered architecture proposed by the Network of Excellence AIM@SHAPE for the definition of semantic-oriented 3D models. In particular the paper proposes the use of meaningful aesthetic features for the evaluation of planar curve signature and for their intent-driven direct modification.